With the rapid pace of technological advancement, traditional approaches to running organizations have become obsolete.
Nowadays, business professionals want to use equipment and tools to make informed decisions. Business Intelligence (BI) has made rapid strides since it emerged in the ‘80s.
Traditional Business Intelligence platforms were high-cost and time-intensive. They were also largely dependent on the IT cell, almost always.
Over the decades, Business Intelligence has undergone a few iterations. The BI you see today is a far cry from its avatar of about 50 years ago.
In those days, it was within the exclusive scope of a motley few in the top echelons of management.
In addition, traditional platforms came with hefty price tags and were also time-intensive applications.
Over time, as technology and computing advanced, Business Intelligence platforms metamorphosed into self-service analytics.
It was open to data analysts and classified by data visualization, preparation, and discovery.
The modern-day version of Business Intelligence platforms is driven by artificial intelligence (AI) and is almost fully automated.
Actually, it would not be wrong to say that BI platforms have grown in tandem with the business requirements of a particular era and with technological progress.
In today’s customer-driven times, where business is defined mainly by a customer’s wants and needs, the success of a company is linked to how fast it responds to a client’s demand.
Real-time interactions across channels require real-time solutions, and that’s why next-gen Business Intelligence deployments are needed.
Most businesses now prefer real-time or near-real-time insights to make fast and accurate decisions.
The next-gen of Business Intelligence is extensible, supports white-labeling, and offers open APIs for customization.
Business users today rely on fully automated Business Intelligence systems without even requiring large data science teams.
The systems have moved from being mere insights platforms to those that can respond to fluid situations.
Next-gen BI applications are disrupting established business analytics processes, just as data warehouses once disrupted disconnected data silos.
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Data warehouses today are equipped to draw inputs from embedded analytics, allowing analytics on “live” data in a business process.
Eventually, the aim is to develop BI platforms that are available to all and require no data science knowledge.
Data experts will still be required
But there will be a few areas where data experts will always be required. One of them is data integration.
If your data is “dirty” or poor, most automated technologies like Deep Learning are bound to fail.
Data scientists will also be required for data integration purposes and deep-dive analytics as advanced ML drives embedded analytics.
Anthony Goldbloom, co-founder and CEO of Kaggle, believes that BU-specific teams will replace centralized Data Science teams.
So, while many data processing tasks are becoming automated, data scientists will shift to the data exploration aspects of business analytics, which, by and large, cannot be performed by machines.
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